Wobbled synthetic discriminant function classification

The task of object classification is complicated by variations in the three-dimensional (3D) object, which translate into distortions in 2-D images. The pattern matching for invariance classification requires a large amount of data and computation time. Proposed here is an efficient distortion invariant object classification algorithm for real-time fringe-adjusted joint transform correlator (FJTC) based automatic target recognition (ATR) system. The proposed classification technique employed fragment-based recognition approach and a new type of synthetic discriminant function (SDF) filter in the generation of distortion invariant correlation filter sets. The optoelectronic FJTC is then used to provide real-time correlation of the filter sets and input under a proper arrangement. This classification method is simple and fast and hence is relevant to be in use by real-time ATR systems. As the conclusion, simulation results are provided to prove the effectiveness of the proposed system in the classification of objects invariant to various distortions.